Real-Time Plant Disease Identification: Fusion of Vision Transformer and Conditional Convolutional Network With C3GAN-Based Data Augmentation

Climate change, adverse weather conditions, and illegitimate farming practices have caused severe damage to the agricultural ecosystem, resulting in significant crop loss in the last decade. One of the major challenges is the breakout of plant diseases that harm the crop in the field. To address this issue, several artificial intelligence and Internet of Things-based systems have been developed for crop monitoring and containment of plant diseases at early stages. In this article, a real-time plant disease identification system is designed using drone-based surveillance and farmer's input. A lightweight plant disease classification model is deployed in the proposed system using a fusion of a vision transformer and a convolutional neural network. The proposed model deploys conditional attention with a statistical squeeze-and-excitation module to efficiently learn the plant disease patterns from images captured under normal and challenging weather conditions. With only 0.95 million trainable parameters, the performance of the proposed plant disease classification model surpasses that of seven state-of-the-art techniques on five public datasets and an in-house developed maize dataset from drone camera-captured images under varying environmental conditions. To provide a better learning experience of real-world data to the model, a generative adversarial network, C3GAN, inspired by cycleGAN, is proposed for data augmentation of the collected maize dataset. The system keeps updating the model parameters based on the feedback of agriculture experts and farmers when new diseases break out or the model's performance deteriorates on unseen data during the surveillance over a period of time.